Export to Snowflake, BigQuery, PostgreSQL or SQL Server

Send a View to another dataset

Send to Dataset pushes the current View's output into another Dataset, which can be in a different Project. Use it to pass processed results downstream in a multi-stage pipeline. Unlike New dataset in the Save a copy popover, it creates a persistent pipeline task that refreshes the Dataset on every run.

1. Click Export, open the Mammoth Managed tab and click Send to Dataset.
2. Choose New Dataset or Existing Dataset.
   • New Dataset: enter a name and pick a Location (a folder, which can be in another Project).
   • Existing Dataset: select a Dataset from the picker.
3. Click Next.
4. On the second step, set what is sent:
   • A filter condition, to send only matching rows.
   • Include hidden columns, to send columns hidden in the grid.
   • The column list, which maps each column to the destination. For an existing Dataset, Hide already-mapped columns helps you find the ones still to map.
   • Each time this runs (see below).
   • Task Position, in the form footer: click the pencil icon and tick Keep at the end of the Pipeline to keep the task at the end of the pipeline.
5. Click Apply.

If other Views also send to the Dataset, an Also fed by note names them. If you switch an existing task from Add a new copy of all rows to Keep this view's rows up to date, the next run also removes the copies this View added on earlier runs.

Auto-sync for this task is switched from the step's options menu.

Get a Mammoth-managed BigQuery or PostgreSQL database

Mammoth provisions a fully managed Google BigQuery or PostgreSQL instance on demand: no credentials to configure, no infrastructure to manage. Each appears as its own card on the Mammoth Managed tab, alongside Send to Dataset and Live link. They need the Pro plan.

1. Click Export, open the Mammoth Managed tab and click the Google BigQuery or PostgreSQL card.
2. Enter the destination table name (auto-filled from your Dataset and View names).
3. Click Next: Generate Table and Copy Credentials.
4. Mammoth creates the table, showing progress messages as it works, then opens the Database Credentials dialog.
5. Use those credentials to connect your BI tool (Tableau, Power BI, Looker) directly.

The managed database table is replaced in full on each pipeline execution (drop and recreate). Managed databases have no Keep-at-end option. A workspace that is over its managed-database limit gets an error when it creates a managed table.

Supported external databases and warehouses

You provide the credentials; Mammoth delivers the data after each pipeline run.

The footer of the export form shows the task's Position (default: end of pipeline) and Update method. Click the pencil icon to change them (see Task position and update method).

Allow Mammoth's IP address in your firewall

Whitelist Mammoth's IP address (34.255.92.234) in your database firewall before clicking Validate Connection. The required IP is shown at the top of the database, warehouse and Elasticsearch forms. See Allow Mammoth through your firewall.

Export to Google BigQuery

BigQuery uses a JSON service account credentials file, not a username and password.

1. Upload your BigQuery service account JSON file. If you have saved more than one connection, pick one from the Connection dropdown; an icon next to it deletes a saved connection.
2. Enter the Table Name.
3. Select your Project Name from the searchable dropdown (populated from your credentials). Each entry represents a project/dataset pair.
4. Choose the Update method in the form footer: Replace, Combine or Merge.
5. If using Merge, select the Reference Column values used as merge keys.
6. Optionally enable Partitioning (requires a DATE column in the View).

Mammoth also writes a reference table named ref_<table> alongside the data table in the BigQuery dataset.

Partition a BigQuery table by date

Partitioning is available when the View contains DATE columns.

1. Tick the Partitioning checkbox and select a date column as the partition key.
2. Choose the partition granularity: Day, Month, or Year.

When editing an existing BigQuery export, the Partitioning checkbox is only enabled if the Update method is Replace. When creating a new export, Partitioning can be set up alongside Combine or Merge too.

Export to PostgreSQL, MySQL, SQL Server or Amazon Redshift

These four databases share the same form.

1. Allow Mammoth's IP address in your firewall.
2. Fill in the fields, or pick a saved connection (below).
3. Choose Replace, Combine or Merge as the Update method in the form footer. With Merge, pick at least one Reference Column.
4. Click Validate Connection.
5. Click Apply.

Validate Connection confirms Mammoth can reach your database: it creates and then drops a test table. Apply only checks that the required fields are filled in. It isn't blocked by a failed or skipped validation, so validating first is the only way to catch bad credentials before the export runs.

Include hidden columns: a checkbox appears if your View has columns that are hidden in the grid. Enable it to include those columns in the export output.

Column names longer than 63 characters make the export fail, so shorten them first.

Reuse a saved database connection

PostgreSQL, MySQL, SQL Server, Amazon Redshift and SFTP exports remember the connection details you use. Once you have one saved, the form shows a Connection dropdown ("Select a saved connection") above the fields.

• Pick a saved connection to fill the form from it. A summary of the connection appears under the dropdown.
• Click Enter details manually to clear it and type new details.
• When you apply new details, Mammoth reuses an identical saved connection if there is one, so repeated exports to the same place don't pile up duplicates.
• If the saved connection was removed, the form says "This saved connection is no longer available. Enter the connection details below."
• If the list can't load, you see "Couldn't load saved connections. You can still enter details manually."

Google BigQuery has its own Connection dropdown for saved key files (see Export to Google BigQuery). To manage saved connections, see Manage saved connections.

Export to Snowflake or Databricks

Snowflake and Databricks share one warehouse form. Table and column names are created exactly as written (quoted), so they are case-sensitive.

Both forms show Mammoth's IP address to allow in your firewall, an Include hidden columns checkbox and Validate Connection. Choose Replace, Combine or Merge as the Update method; with Merge, pick at least one Reference Column.

Export to Elasticsearch

Whitelist Mammoth's IP address (34.255.92.234) in your Elasticsearch firewall, then click Validate Connection before Apply. Apply isn't gated on a successful validation, so validating first is the only way to catch bad credentials before the export runs.

Include hidden columns is available if your View has hidden columns. Elasticsearch is replaced on every run, so it can't send only new rows.

Limits

Troubleshooting database exports

Validate Connection fails

Cause: Mammoth's IP address (34.255.92.234, shown in the form) is not allowed in your database firewall, or the credentials lack permissions. Validate Connection checks for CREATE TABLE and DROP TABLE permissions.

Fix: Add the IP address to your firewall allowlist. Also grant INSERT permission, which the data delivery needs once the export runs.

Schema conflict error

Cause: This applies to database and warehouse exports using the Combine or Merge update method, where the destination table persists between runs. The View's columns no longer match the destination table's columns, so the run fails.

Fix: Delete and recreate the destination, or adjust the View to match the original column structure.

Merge fails

Cause: The Reference column is missing from the View or the destination table, or the names or data types don't match.

Fix: Confirm the Reference column exists in both with matching names and compatible data types.

The export fails on long column names

Cause: A column name is longer than 63 characters.

Fix: Shorten the column name in the View (see Rename, format and sort columns).

For a destination that doesn't update, see Troubleshooting exports.

Related

• Export data after every pipeline run: plans, when it runs, run history and Auto-sync.
• Send to files, apps and APIs: SFTP, SharePoint, Google Sheets, Email, Tableau and REST API.
• Live links: a CSV URL for Power BI and Excel.
• Allow Mammoth through your firewall: IP addresses to allow.
• Manage saved connections: reuse database credentials.